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Updated: Jan 20, 2026

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
Published on: September 8, 2023
Kyungho Won1, Moonyoung Kwon1, Sehyeon Jang1
1School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, South Korea.
Researchers developed a new method to predict brain-computer interface (BCI) performance. By analyzing rapid serial visual presentation (RSVP) task features, they created a predictor that improves accuracy and speed for P300 speller systems.
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